基于小波包法与CSSD的P300特征提取方法

针对P300电位信号微弱、抗干扰能力差、识别率低等问题,提出一种小波包变换(wavelet packet transform,WPT)与共空域子空间分解法(spatial subspace decomposition,CSSD)相结合的特征提取方法,即WPCSSD法.首先,对脑电信号进行叠加平均以提高信号的信噪比;其次,使用小波包法对脑电信号进行滤波,并依据P300电位的有效时频信息重构脑电信号;然后,求取其AR模型功率谱,并基于CSSD法构造空间滤波器,获得能体现P300电位时-频-空特征的特征向量;最后,以支持向量机为分类器进行分类.实验结果表明:本方法具有较强的抗干扰能力和自适应能力,在国际BCI竞赛数据集上获得了95.22%的分类正确率,证明了本方法的正确性和有效性.

P300 Feature Extraction With Wavelet Packet Transform and CSSD

P300 potential is weak and has poor anti-interference ability and low recognition rate. Based on wavelet packet transform(WPT) and common spatial subspace decomposition(CSSD),a feature extraction method,denoted as WPCSSD,was proposed in this paper. First,the EEG was preprocessed by the overlapping average algorithm to improve its signal-to-noise ratio. Second,the EEG was filtered and reconstructed by WPT according to the time-frequency information of P300. Third,the power spectrum based on AR model was computed,and a spatial filter with CSSD was applied to extract the spatial feature of P300. The feature vector can therefore reflect the time-frequency-space information of P300 generally. Finally,the support vector machine was used for classification. Results show that WPCSSD has better anti-interference and adaptive ability,and the recognition accuracy is 95. 22% in data sets of BCI competition. The correctness and validity of the method are proven.